Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning

Diffuse gliomas are the most common primary brain tumors and they vary considerably in their morphology, location, genetic alterations, and response to therapy. In 2016, the World Health Organization (WHO) provided new guidelines for making an integrated diagnosis that incorporates both morphologic and molecular features to diffuse gliomas. In this study, we demonstrate how deep learning approaches can be used for an automatic classification of glioma subtypes and grading using whole-slide images that were obtained from routine clinical practice. A deep transfer learning method using the ResNet50V2 model was trained to classify subtypes and grades of diffuse gliomas according to the WHO’s new 2016 classification. The balanced accuracy of the diffuse glioma subtype classification model with majority voting was 0.8727. These results highlight an emerging role of deep learning in the future practice of pathologic diagnosis.

Empathy Bot: Conversational Service for Psychiatric Counseling with Chat Assistant

There are earlier studies for psychiatric counseling using chat bots. These studies have not considered the user's emotional status and ethical judgment to provide interventions. This paper proposes an intelligent assistant for psychiatric counseling that understands dialogues using high-level features of natural language understanding, and multi-modal emotion recognition. A response generation model using machine leaning provides suitable responses for clinical psychiatric counseling.